Millions of individuals worldwide suffer from various forms of seizure disorders. In most cases, these individuals can be treated with standard regimes of medications (anti-convulsants) which offer acceptable results. Less commonly, an individual may have an "intractable" form of epilepsy in which most reasonable forms of medical therapy have failed to provide adequate relief. Surgical excision of the portion of the cerebrum responsible for the abnormal electrical activity is one alternative to medical therapy in such intractable cases. The popularity of the surgical treatment of epilepsy has increased substantially since the release of a National Health Institute report supporting the efficacy of surgical intervention in appropriately selected cases. One of the key elements in selecting patients is an understanding of the number and anatomic location of sites of abnormal electrical activity. To the extent that there are multiple or widely distributed abnormal sites, surgical intervention is less likely to succeed. Thus, precise knowledge of the number and location of intracerebral sources of seizure activity is critical to successful surgical intervention.
There are a number of other surgical procedures, e.g., involving removal of cerebral tumors, where determining the location of the tissue of origin of activity or control (for example, hand, face or foot movement) is important in guiding the surgeon's efforts. Similarly, the value of evoked potential studies would be enhanced by a more detailed knowledge of the anatomic origin of the multiple components of the evoked potential (EP) waveform. While numerous methods have been proposed to determine the location of EP components, none has succeeded in handling the analysis of multiple simultaneous sources.
A variety of assessment tools are used by neurologists to assess the functional status of different regions of the brain, with the most common being the electroencephalogram (EEG). From the view point of the neurologist, it would be desirable to be able to determine the location of sources of electrical activity within the brain using electrical field potential measurements made at the scalp. Multiple sensors placed at different locations on the scalp may be employed to sample the spatial distribution of the electric potential a the scalp surface. To allow sources within the brain to be located, it is necessary to use a model that relates the underlying neural activity to the distribution of potential as measured by the surface sensors. Such models rely on the solution of the "forward" problem. The forward problem may be simply described as calculation of the scalp potential due to a known neural source in a known location. The solution to the forward problem requires assumption of a model for the geometry and physiological properties of the head.
The most commonly used model of the head is a three shell spherical model (scalp, skull, brain parenchyma). More sophisticated models derived from magnetic resonance imaging (MRI) or other imaging of the head offer increased realism at the expense of considerable additional testing and computation. Calculation of the intracranial source distribution responsible for an observed scalp potential field is called the "inverse" problem.
Electrical activity detected from scalp electrodes does not generally reflect the contribution of a single neuron, but, more typically, hundreds or thousands of neurons firing nearly synchronously. The geometric arrangement of these neurons can profoundly effect the "net equivalent" dipole detected from a distant recording site. In the extreme, complete cancellation of electrical activity can occur with certain cellular geometries, resulting in a zero mean moment, as measured from a distance. The inability to detect the electrical response from certain classes of geometric arrangements is an intrinsic weakness of most signal processing algorithms. In contrast, the present invention is dependent upon both first order (mean) and second order (variance) statistics. Thus, zero mean equivalent dipoles can be detected as long as there is a non-zero variance component. Control of the contribution of first versus second order statistics to the output of the localization algorithm can be manipulated by changes in stimulus parameters or by changes of algorithm terms.
Depending on the specific source (e.g. spike, visual evoked potential, etc.), the equivalent dipole will likely shift over time. By performing the localization algorithm at any instant in time and repeatedly applying the algorithm over time, a representation of source spatial movement with time can also be generated.